Multi-Objective Machine Learning - Yaochu Jin
-20% with code BOOKS
Shipping in 10-16 days
30-day return policy
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate ... Full description
You May Also Like
TOP
Six Not-so-easy Pieces: Einstein's Relativity, Symmetry, and Space-Time
Richard P. Feynman, Robert B. Leighton, Matthew Sands
-20% with code BOOKS
In stock at our supplier
TOP
Six Easy Pieces: Essentials of Physics Explained by Its Most Brilliant Teacher
Richard P. Feynman, Robert B. Leighton, Matthew Sands
-20% with code BOOKS
In stock at our supplier
TOP
Behave: The Biology of Humans at Our Best and Worst
-20% with code BOOKS
In stock at our supplier
TOP
Determined: The Science of Life Without Free Will
-20% with code BOOKS
In stock at our supplier
TOP
Beyond Inheritance: Our Ever-Mutating Cells and a New Understanding of Health
-20% with code BOOKS
In stock at our supplier
TOP
The Age of Alchemy: How Early Innovators Shaped Modern Chemistry
-20% with code BOOKS
In stock at our supplier
TOP
The Secrets of our DNA: How Genetics has Changed the World
-20% with code BOOKS
In stock at our supplier
TOP
Introduction to Quantum Mechanics
Darrell F. Schroeter, David J. Griffiths
-20% with code BOOKS
In stock at our supplier
TOP
Why We Sleep: Unlocking the Power of Sleep and Dreams
-20% with code BOOKS
In stock
TOP
Biology: A Global Approach, Global Edition
Neil Campbell, Rebecca Orr, Lisa Urry, Peter Minorsky, Michael Cain, Jane Reece, Steven Wasserman
-20% with code BOOKS
In stock at our supplier
TOP
Why Machines Learn: The Elegant Maths Behind Modern AI
-20% with code BOOKS
In stock at our supplier
TOP
A Short History of Nearly Everything 2.0
-20% with code BOOKS
In stock at our supplier
TOP
Astrophysics for People in a Hurry: Essays on the Universe and Our Place Within It
-20% with code BOOKS
In stock at our supplier
TOP
The Rise and Fall of the Dinosaurs: A New History of a Lost World
-20% with code BOOKS
In stock at our supplier
TOP
The Rise and Reign of the Mammals: A New History, from the Shadow of the Dinosaurs to Us
-20% with code BOOKS
In stock at our supplier
TOP
Learn Faster, Perform Better: A Musician's Guide to the Neuroscience of Practicing
-20% with code BOOKS
In stock at our supplier
TOP
A Brief History of Time: From the Big Bang to Black Holes
-20% with code BOOKS
In stock at our supplier
TOP
Thing Explainer: Complicated Stuff in Simple Words
-20% with code BOOKS
In stock at our supplier
Description
Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
More Information
| Author | Yaochu Jin |
|---|---|
| Publisher | Springer Science & Business Media |
| Release year | 2006 |
| Cover type | Hardcover |
| EAN | 9783540306764 |